General purpose compute clusters are used by a wide range of organizations to deliver the necessary computational power for their processes. In order to manage the shared use of such clusters, scheduling policies are installed to determine if and when the jobs submitted to the cluster are executed. Value-based scheduling policies differ from other policies in that they allow users to communicate the value of their computation to the scheduling mechanism. The design of market mechanisms whereby users are able to bid for resources in a fine-grained manner has proven to be an attractive means to implement such policies. In the clearing phase of the mechanism, supply and demand for resources are matched in pursuit of a value-maximizing job schedule and resource prices are dynamically adjusted to the level of excess demand in the system. Despite their success in simulations and research literature, such fine-grained value-based scheduling policies have been rarely used in practice as they are often considered too fragile, too onerous for end-users to work with, and difficult to implement. A coarse-grained form of value-based scheduling that mitigates the aforementioned disadvantages involves the installation of a priority queuing system with fixed costs per queue. At present, however, it is unclear whether such a coarse-grained policy underperforms in value realization when compared to fine-grained scheduling through auctions, and if so, to what extent. Using workload traces of general purpose clusters we make the comparison and investigate under which conditions efficiency can be gained with the fine-grained policy.